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Social and Economics Review of BLM DEIS on Oil and Gas Leasing

Dr. John Loomis*

Dept of Agricultural and Resource Economics

Colorado State University

Fort Collins, CO 80523

January 6, 2016

*The views expressed in this report are those of the author and do not necessarily reflect

the views of Colorado State University. The author is solely responsible for the content of

this report.

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Introduction

A comprehensive social and economic analysis of the classic natural resource

development versus preservation debate helps to avoid portraying the choices as

“environment versus the economy”. A comprehensive analysis often shows that

preservation of a natural environment provides several types of economic values to

people whether they are:

residents of the area (and reveal their benefits in house price premiums to live

near the area),

visitors (as reflected not only in their trip expenditures in the area but the visitor’s

own benefits in excess of their trip expenditures),

non-visiting general public, some of which may derive value from just knowing

that an area exists in its natural state and if protected will be passed onto future

generations as it exists today.

Currently, the primary monetary quantification in the BLM DEIS is related to oil and gas

development. However, all the alternatives have an effect on big game habitat (hence

hunting), visual resources, recreation resources, and property values. This report provides

literature and examples of how the economic values of these other resources presented in

this report can be incorporated into an economic analysis, included in the FEIS, and

would provide additional support for Alternative #4 as the proposed action.

This report will review BLM’s socioeconomic analysis and show how it can be

improved upon by referencing additional literature, and in some cases demonstrate how

an improvement in analysis would show the relative magnitude of several types of

economic benefits associated with Alternative #4. The goal of this report is to show that

even a little more detail on the qualitative analysis of the economic benefits of

Alternative #4 would show that the DEIS alternatives are not examples of the

“environment versus the economy” as might currently be perceived by some, but rather a

trade-off regarding the type of economic values that can be provided by National Forest

lands in the Thompson Divide area.

Executive Summary of Key Points

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1. BLM’s quantitative portion of its Socioeconomic analysis primarily focuses on oil and

gas income, employment and tax revenues and performs only a cursory descriptive

evaluation of most other environmental and non market values. Below I perform an

analysis of elk hunting to provide an analysis of the non market losses from Alternatives

#1, #2 and #3 on elk hunting.

2. Minimal description of non market values in Chapter 3 and Chapter 4, despite a BLM

Instruction Memorandum requiring the agency to at least do a comprehensive qualitative

analysis of non market values. For example, while BLM cites the BBC report (2013) on

use values, BLM does not include discussion of ecosystem services or passive use values

of the Thompson Divide despite these topics also being covered in the BBC (2013)

report.

3. BLM’s visual analysis of the impacts of oil and gas development in the Thompson

Divide is focused too narrowly, frequently focusing on just surface disturbance. The

visual analysis does not discuss the literature on potential changes in recreation use and

visitor spending in the analysis area with development. More detail on the linkage is

provided below and can be cited by BLM.

4. Property values are given only a general statement when it is, as I show, quite easy to

at least add some simple median house prices along with an economic interpretation.

5. Passive Use/Non Use Values (Existence/Bequest) of possible loss of Roadless Areas.

While there is some contention whether Alternatives #1, #2 and #3 will allow

construction of roads in Roadless Areas in the DEIS study area, a simple Benefit Transfer

is conducted on the potential loss in Non Use values if Roadless Areas are disturbed. My

comments provide such a calculation. As noted in #3 above, BBC (2013) specifically

discussed Passive Use Values associated with the Thompson Divide area but these are

not referred to by BLM in the DEIS. BLM needs to acknowledge passive use values of

the roadless areas within the Thompson Divide (Zone 3) and that Alternatives #1, #2, and

#3 would reduce those values.

Taken together these limitations in BLM’s DEIS understate the adverse economic

effects of Alternatives #1 and #2 relative to Alternative #4. Additional documentation of

the market and non market environmental effects of the alternatives are provided in the

details of this report. Incorporation of the additional documentation in the FEIS would

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allow BLM to counter the impression given in the Socioeconomic analysis of the DEIS

that the choice before them and society is “Environment versus the Economy”. Rather,

Alternative #4 provides several types of economic benefits that can be better

documented.

I. Background on BLM Study Areas

The BLM DEIS covers a broad geographic area ranging from west of Carbondale,

Colorado all the way to Mesa, Colorado. The primary focus of our analysis is the

Thompson Divide area (corresponding to Zone 3 in the BLM DEIS) although some of the

analysis may be applicable to oil and gas leasing to the other zones covered in the DEIS.

What makes the Thompson Divide area somewhat different than the other areas is

its proximity to communities that value largely undeveloped landscapes. These

landscapes provide many benefits (e.g., watershed protection), including opportunities for

undeveloped and dispersed recreation with a low density of use allowing for solitude (at

least as compared to many other areas on the White River National Forest and Roaring

Fork Valley). These values as well as the “quiet and remoteness from the sights and

sounds of human development” were noted in the WRNF Oil and Gas FEIS (ROD-6).

As discussed in Section D of this report, residents pay a premium for housing in

the Carbondale area compared to other areas closer to I-70 and to oil and gas

communities (e.g., Rifle). There is a well developed literature in economics that suggests

that a housing price premium is related to perceived quality of life in that area. Public

comments on the WRNF Oil and Gas FEIS suggest that a significant part of that high

quality of life is related to the Thompson Divide area. This was noted by the U.S. Forest

Service in the Record of Decision on the WRNF Oil and Gas FEIS when that document

noted that the Thompson Divide area is a “…special place” (WRNF, FEIS-ROD-6) that

contributes a “sense of place” to the communities that surround it.

Some of this sense of place is arises from actual recreation use of an area and

some from the scenic backdrop that these undeveloped lands provide to the surrounding

communities. With respect to recreation use, BBC estimates that the grazing lands,

wildlife habitat, fishing and other recreation within the Thompson Divide support about

$30 million in economic activity and about 150 jobs.

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These statements about the Thompson Divide area are an example of a larger western

rural U.S. and U.S. wide pattern in rural areas. In particular, Headwaters (2011)

concluded that “Amenities and quality of life are cornerstones of the long-term economic

viability of these (western) rural regions”. A thorough statistical analysis of the role of

natural amenities and quality of life in economic growth in rural areas of the U.S.

(published in American Journal of Agricultural Economics) found “Of particular interest

is the general conclusion that all statistically significant amenity attributes are positively

related to economic growth.” (Deller, et al. 2001: 363). These authors go on to conclude

that “Rural areas endowed with key natural resource amenities can manage those

resources to capture growth more effectively. This may entail expansion beyond policies

that have historically been focused on extraction of the resource base. Given the

recreational uses of these resources, the future growth and development potential of

many rural areas may be additionally tied to a range of tourism activities” (Deller, et al.

2001: 363).

There is also a short-term vs long term trade off associated with relying on

extraction of non-renewable resources as a significant part of an area’s economy: in the

short run while there is oil and gas to be extracted, there is a short run economic stimulus

added to the economy. However, as the State of Alaska found after 20 years of the oil

production in Prudhoe Bay, production begins to decline (Oil and Gas Journal, at

http://www.ogj.com/articles/print/volume-98/issue-40/special-report/data-shows-steep-

prudhoe-bay-production-decline.html) and it takes with it decreasing government

revenues tied to oil. This short run versus long run trade off is noted by Headwaters

(2013: 17): Our findings call into question the understandable, but mistaken assumption

that long-term oil and gas development is a clear economic advantage for host

communities. Our study does not question the idea that oil and gas activity can have a

strong immediate positive impact on employment and income, but it does suggest there

are negative effects when oil and gas extraction plays a major role in a local economy for

a long-period of time.

BLM should include a short discussion of the implications of Headwaters’ two reports

(2011) and (2013) and Deller, et al. conclusions findings to the trade-offs facing the

Thompson Divide area.

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As noted in the BBC’s report on the Thompson Divide and Walsh, et al. (with

regard to Colorado roadless areas) undeveloped land provides what today is called by

BBC Passive Use Values and Walsh, et al. referred to as Preservation Values. The bulk of

these values are the benefits that protection of these lands provide to just know they exist

in their undisturbed state and will be handed down as a bequest to future generations.

More detail on quantifying these values are provided in Section E.

II. Detailed Comments

A. Narrowness of Analysis by Focusing Just on Oil and Gas Surface Disturbance

BBC (2013:11) estimates 18,000 elk hunting days and 6,000 deer hunting days per year

that accounts for 72 jobs throughout the state of Colorado, with half of those 36 jobs

being in the local economy surrounding the Thompson Divide (essentially Zone 3). Thus,

big game hunting, particularly elk hunting should be given greater recognition by BLM in

the FEIS. Likewise, BBC (2013: 5) estimates there are 138 jobs supported by Thompson

Divide recreation visitors spending in the economy. Thus, outdoor recreation warrants

attention by BLM.

The assuming away of much impacts to recreation by the BLM’s DEIS is due to

the DEIS focusing too narrowly on just the acres of surface disturbance, and ignoring

what I would call oil and gas (O&G) development’s extended “footprint” and “area of

influence” on recreation beyond the immediate areas physically disturbed. The extended

footprint is related to BLM’s acknowledged impacts of oil and gas activity on off-site

impacts in Chapter 4, section 4.13 (4.13-4):

“For lands that are subsequently developed, adverse impacts to recreation resources

would be greatest during the initial construction phases when vehicle traffic, human

activity, and noise are the greatest. Adverse impacts may result from changes to the

existing landscapes through introduction of new industrial features such as access roads,

well pads, facilities, pipelines, and utility corridors. Associated impacts from initial

disturbances could include noise, lights, dust, smell, construction equipment, and

construction traffic. Changes in traffic volume during construction could negatively

affect recreational users’ experiences. These adverse impacts also would potentially

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affect lands adjacent to leasing areas, not to just the leasing areas themselves.”

(emphasis added)

While BLM acknowledges this spillover effect (and mentions “avoidance of

wildlife”—BLM DEIS page 4.7-3), BLM does not appear to fully incorporate this

extended area when analyzing Alternatives #1 and #2 as it focuses heavily on acres of No

Surface Occupancy--NSO. If BLM is focusing primarily on acres of surface disturbance

BLM substantially underestimates the impacts of the O&G activity on recreation. This

narrow analysis of recreation then carries over to a narrow and qualitative economic

analysis that essentially dismisses significant effects to hunting and other recreation. I

address this narrowness in Section B for hunting and Section C for other recreation.

B. My reassessment of the Effects of Oil and Gas Development on Elk Habitat

and Elk Hunting by Alternative

BLM notes in Chapter 3 on terrestrial wildlife that:

“Big Game Analysis Area: The big game analysis area consists of the Game

Management Units (GMUs) that are crossed by the lease boundaries. Sensitive habitat is

typically considered the limiting factor for big game populations, therefore additional

focus will be given on these areas (e.g., winter range, transition range, migratory

corridors, fawning and calving areas and summer range) within the GMUs.” BLM DEIS

page 3.7-1.

Based on this section, it would seem there is a strong and direct relationship

between impacts to sensitive habitat and big game populations. This is especially true for

elk, since Area 3 (Thompson Divide area) provides thousands of acres of sensitive elk

habitat. Specifically, Zone 3 contains 13,523 acres of what BLM calls Production Area,

18,063 acres of Summer Concentration Area, 100 acres of Severe Winter Range and

2,112 acres of Winter Range for a total of 33,798 acres of sensitive habitat (Page 3.7-17).

Only 7% of the Elk Production Areas are covered under No Surface Occupancy (NSO) in

Alternative #2 and only 41% have Timing Limitation (Page 4.7-22). Thus, Alternative #2

does not provide protection for 20,000 acres of Sensitive Elk habitat. Alternative #1

leaves even more elk habitat unprotected.

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However, these 20,000 acres of sensitive elk habitat may underestimate the total

elk habitat at risk. In particular, it is not clear whether BLM is focusing just on the area of

surface disturbance associated with the leases (as the focus is on No Surface

Occupancy—NSO), and not the broader area of elk avoidance of sights and sound of

human activities associated with oil and gas wells. BLM notes (DEIS page 4.7-7) that the

WRNF did look at behavioral disturbance as well as physical impacts. This is appropriate

because as noted by Sawyer, et al (2006: 396) in an article on a less human sensitive

species than elk (mule deer) and natural gas fields in Wyoming, “Changes in habitat

selection appeared to be immediate (i.e., year 1 of development), and no evidence of well-

pad acclimation occurred through the course of the study; Lower predicted probability of

use within 2.7 to 3.7 km of well pads suggested indirect habitat losses may be

substantially larger than direct habitat losses.” (emphasis added).

It is not completely clear in the BLM DEIS that BLM included these behavioral

responses. It is not unusual for BLM to estimate these indirect habitat losses beyond just

the surface disturbance. BLM, in the Buffalo Field Office of BLM in Wyoming,

developed maps and analysis that showed the elk avoidance areas beyond those of just

surface disturbance from oil and gas. In particular elk avoided habitat areas from where

elk could see human activity. BLM used this information in its analysis of oil and gas in

its Environmental Analyses for Williams Production Company (and oil and gas

company), Carr Draw in 2009, 2010 and 2011 (BLM, 2010, 2011). The link for the 2009

analysis is:

http://www.blm.gov/style/medialib/blm/wy/information/NEPA/bfodocs/pods/williams.Pa

r.21794.File.dat/CarrDrawVaddII_EA.pdf

For the purposes of my analysis I will give BLM the benefit of the doubt that they

did include the behavioral impacts to these sensitive habitats they alluded to and not just

include physical impacts to habitat (page 4.7-7 of the DEIS). However, BLM needs to

clarify whether they did or did not include the indirect behavioral effects like the WRNF

did, when BLM finalizes its FEIS.

Given that BLM indicates that “Sensitive habitat is typically considered the

limiting factor for big game populations,..” it would seem reasonable to infer that

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changes in sensitive habitat would translate into changes in big game populations such as

elk. I selected elk as this species has the largest changes in habitat with each alternative

and the most sensitive habitat. When fish and game agencies set hunting regulations

(length of seasons, number of permits, etc.), it generally uses information on animal

populations to determine length of the season and number of permits to issue. Thus, one

would expect there is a strong relationship between elk populations, the resulting elk

harvest and associated changes in elk hunter days within the impact areas using BLM’s

data in the DEIS.

Using the above as background, I will calculate by alternative: (1) the percentage

changes in sensitive elk habitat presented in the DEIS by alternative; (2) convert percent

changes in sensitive elk habitat to changes in elk harvest; (3) how changes in elk harvest

change elk hunter days by alternative; (4) the changes in elk hunter days and associated

non market values by alternative. This analysis is provided so that BLM can incorporate

these results directly into their Final EIS.

The first step is to determine if there is an empirical linkage from elk harvest to

hunter days in the DEIS study area. This is an empirical question for which there is data

available from the DEIS (Table 3.13-4, updated to 2014 using Colorado Dept of Parks

and Wildlife data).

Estimating the Effects of Elk Harvest on Hunter Days

The data to test the whether the number of elk harvested influences elk hunter days

utilized data from the DEIS (Table 3.13-4, years 2013-2009, updated to 2014 using

Colorado Dept of Parks and Wildlife data). The data consisted of harvest and hunter days

at the four Game Management Units (GMU’s) identified in Table 3.31-4. A trend

variable was included as BLM noted and the data suggested there was an upward trend in

elk hunter days. A least squares regression was estimated and a statistically significant on

coefficient (1% level) of elk harvest on hunter days. This indicates that changes in elk

harvest results in changes in hunter days (see Table 1). Specifically each elk harvested

results in 6.185 more hunter days. The overall explanatory power of the regression model

is reasonably good with an adjusted R square of 70%, indicating that 70% of the variation

in hunter days could be explained just by number of elk harvested and the trend. It should

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also be noted that the Trend variable indicates significantly increasing demand for elk

hunting over the 2009-2014 time period. Thus the demand for elk hunting is likely to

increase significantly at these Game Management Units in absence of reductions in

habitat. Thus oil and gas leasing in Zone #3 will have an even larger impact in the future

than at present. Table 1, presents the least squares regression results.

Table 1. Multiple Regression of the Influence of Elk Harvest on Hunter Days

__________________________________________________

Dependent Variable: Hunter Days Method: Least Squares Regression Date: 12/30/2015 Sample: 1 24

Variable Coefficient Std. Error t-Statistic Prob. Constant 11146.24 1403.345 7.942621 0.0000

HARVEST 6.185214 0.831771 7.436197 0.0000 TREND 557.8872 257.5679 2.165981 0.0420

R-squared 0.729280 Mean dependent var 19527.08

Adjusted R-squared 0.703497 S.D. dependent var 3918.010 S.E. of regression 2133.437 F-statistic 28.28551 Prob(F-statistic) 0.000001 ____________________________________________________________

Given that we have a statistically significant relationship between harvest and hunter

days, the next step is to compare alternatives on the percent of sensitive elk habitat that is

protected (as that determines long term elk population, and hence long term sustainable

elk harvest) and how that changes by DEIS Alternative.

While Table 2 was constructed from information in the DEIS, Table 2 provides a

summary for Zone 3 that BLM should include in the FEIS as it more clearly compares the

effects of each Alternative on elk habitat than the current DEIS.

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Table 2 Percent of Elk Production Areas & Elk Summer Concentration

Areas in Zone 3 Protected by Alternative

Alt #1 Alt # 2 Alt #3 Alt #4 Alt #4

NSO/TL NSO/TL NSO Cancelled Cancelled +NSO

Production

13,523 ac

4/39% 7%/41% 83% 72% 90%

Summer

18,063 ac

Not

reported

5% 93% 57% 100%

NSO is No Surface Occupancy; Timing Limitation on season of O&G operation

As can be seen in this table, Alternatives #1 and #2 leave a great deal of sensitive habitat

unprotected from oil and gas operations. Given BLM’s statement that sensitive habitat is

a limiting factor, and in the long run elk populations stabilize around the carrying

capacity of their limiting habitat, long term elk populations and sustainable elk harvests

would be expected to drop accordingly. Since there is more complete data for the Elk

Production Area, I will use the percentage of habitat protected with Elk Production

habitat as an indicator of overall percent of sensitive habitat protected with each

alternative. Given the availability of the data BLM presents in the DEIS I will use percent

No Surface Occupancy (NSO) as the determinant of elk populations and elk harvest

except for Alternative #1 and #2 since BLM will rely more heavily on Timing

Limitations (seasonal closure to O&G activity) than NSO in Alternatives #1 and #2. Thus

the long term elk population and therefore, elk harvest in Alternative #1 is expected to be

39% of current harvest, and 41% for Alternative #2. Alternative #3, relies more upon

NSO and maintains 83% of current habitat, hence 83% of current elk population and

therefore elk harvest. Finally, with Alternative #4 which cancels all or part of 25 leases in

Zone 3 the combination of lease cancellation and NSO, results in 90% of Elk Production

habitat being protected, and hence 90% of current elk populations and harvests. The

current situation would be 100% (the full 33,798 acres of sensitive elk habitat in Zone 3,

the Thompson Divide area and the elk harvest associated with that).

In order to calculate the number of hunter days with each alternative, the

regression equation in Table 1 was applied using the percent of sensitive habitat protected

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with each alternative as the percent of current elk harvest (since sensitive habitat is the

limiting factor to population, it is also the limiting factor for long run elk harvest). This is

calculated as the relevant percentage (Alt #1=39%; Alt #2=41%; Alt #3=83%; Alt #4=

90%) times the regression equation estimated current elk harvest (specifically 33,798

acres of all four types of sensitive elk of habitat in Zone 3—the Thompson Divide area

times .003699 which is the average elk harvest per acre of habitat from 2009-2013 from

Table 3.13-4 in BLM DEIS1). The Trend variable was set at 10, to reflect the year 2018.

This year was chosen to allow for equilibration of the elk population to the new level of

Elk Production habitat. Table 3 presents the results.

Table 3. Elk Hunter Days in Zone 3 (Thompson Divide) by Alternative

Alt #1 Alt #2 Alt #3 Alt #4 Est Current

Hunter Days (HD)

17,027 17,042 17,367 17,421 17,498

Drop in HD from

Current

-472 -456 -131 -77 -

As can be seen, there is a loss of about 472 elk hunter days from Alternative #1 compared

to the estimated current number of elk hunter days in Zone 3 now. The decrease in elk

hunter days gets smaller and smaller as the alternative protects more and more elk

habitat. Next we turn to the non market economic consequences of the reductions in elk

hunter days to the hunters themselves.

Non market values: Benefits to the Elk Hunters Themselves

BLM is “strongly encouraged” by its own Instruction Memorandum 2013-131 to

incorporate “A quantitative analysis of nonmarket values in EIS-level NEPA analyses…”

The easiest way for BLM to perform such a quantitative analysis is using Benefit

Transfer. For big game hunting there is a Benefit-Transfer Toolkit of Loomis and

Richardson (see http://dare.agsci.colostate.edu/outreach/tools/#BTT) that provides an

1 Ideally we would use the total amount of elk habitat in Zone #3, as the .003699 is calculated by dividing

elk harvest in the Game Management Unit (GMU) by the total acres of the GMU. Using just the acres of

sensitive elk habitat understates the amount of habitat, and hence the number of elk harvested in Zone #3.

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average net economic benefit to big game hunters in the intermountain region based on

141 big game hunting studies in that region. The average value is $71.37 ($83.81in

$2014). This is the added benefits the hunter receives themselves after all their travel

costs have been paid. This is the measure of benefit used by Federal agencies when

performing an economic efficiency analysis (as compared to an IMPLAN economic

impact study).

At $83.81 per hunter day, Table 4 presents the results of the losses in nonmarket

values from the lost elk hunter benefits.

Table 4. Annual Elk Hunter Benefits by Alternative for Zone 3

_______________________________________________________________________

Alt #1 Alt #2 Alt #3 Alt #4 Current

Elk Hunter Days 17,027 17,042 17,367 17,421

17,498

Elk Hunter

Benefits $ 1,427,007 $1,428,303 $1,455,522 $ 1,460,058

$1,466,539

Drop in Benefits $ (39,533) $ (38,236) $ (11,017) $ (6,481) $ -

________________________________________________________________________

These annual losses in elk hunter benefits should be incorporated into the Final EIS.

C. Broadening Visual Impact Analysis and Linking to Recreation Use

While there was a Scenic Resource Analysis in the DEIS it appears focused on impacts to

Scenic Resources only within the Thompson Divide area. However, a broader visual

resource analysis may show that there would be the visibility of oil and gas drilling and

production from various commonly used areas within and outside the Thompson Divide

area. For example, it would be worth mentioning in the FEIS the potential visibility of oil

and gas activity and disturbance from the heavily used McClure Pass area, from the top

of the Sunlight Ski area, from Sunlight Peak, from Ridge Lakes trail and the Spring

Gulch Nordic Area.

The reasons for including the visual effects is that there are economic

repercussions of replacing undisturbed natural landscapes with pockets of industrial

activity. For example, Orens and Seidl (2004) found that even conversion of undeveloped

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land, open space, and ranch lands to tourism infrastructure and second homes may reduce

winter tourism in Gunnison County (home of Crested Butte ski area) but as much as 40%.

The adverse impacts of the reduction in visitation were on the order of $14 million

annually and 350 jobs. While the absolute magnitude of the effects may be smaller for the

Sunlight Ski Area and cross country skiing there (and Spring Gulch Nordic Area), the

effects of visible oil and gas drilling and production is likely to have even larger

percentage impacts on visitation than the visual impact of second homes. Ellingson, et al.

(2006) found similar percentage reduction (50%) in summer visitors to Routt county

(home of the Steamboat ski area that offers mountain biking and hiking during the

summer) if ranch lands were converted to urban uses. Again, we would expect at least

this much change in visitation with industrial facilities such as oil and gas drilling and

production operations. There would be a resulting decrease in employment and non

market values associated with losses in recreation use due to industrial development. At a

minimum BLM should acknowledge the potential relative magnitude (e.g., percent

changes) of economic impacts associated with Alternative #1 and #2 (citing these two

studies), in order to provide a more balanced socioeconomic assessment of the potential

economic losses associated with Alternatives #1 and #2.

D. Property values are given only a qualitative analysis. However, median house price

data in the region is readily available and is revealing of quality of life differentials

associated with oil and gas drilling. According to www.city-data.com estimated median

house/condo prices in Carbondale is $471,531 while in Rifle where oil and gas

development is common it is $252,922. Economic models such as the hedonic property

model (Taylor, 2003; Freeman, 2003) have established and demonstrated that house price

differentials reflect the amenities and disamenities of an area. While not all the price

differential is due to the presence of oil and gas development in Rifle, the hedonic

property method would indicate that certainly some of the nearly $220,000 difference in

house prices is related to the absence of oil and gas in Carbondale and the higher quality

of amenities there (e.g., air quality, quiet, surrounding natural environment). Presence of

an estimated 51 wells in Zone 3 (adjacent to Carbondale) and associated heavy truck

traffic by Carbondale will certainly result in some diminution of what property values

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would have been in absence of these 51 wells. BLM should present these statistics on

home prices, note there is an economic method to interpret the impact of Alternative #1

and #2 oil and gas developments on house prices, and especially to an area currently with

a minimal oil and gas wells.

Effect of Truck Traffic on Quality of Life: Property Values and Accidents

Both the WRNF FEIS and BLM DEIS quantify truck traffic associated with leasing.

BLM did this by alternative. However, BLM’s analysis just mentions that the effect of

the increased truck traffic with Alternatives 1, 2 and 3 has a negative impact to property

values. Likewise, a benefit of Alternative 4 is the avoided reduction in property values.

However, there is an existing literature identifies socio-economic costs associated with

increased truck traffic. In particular, an article in the Road Engineering Journal (1997)

http://www.usroads.com/journals/p/rej/9710/re971004.htm

found that there was a systematic effect of truck traffic noise on property values. Large

trucks in urban fringe areas resulted in a larger reduction in property values than in urban

areas. This should be noted by BLM as a cost of Alternatives 1, 2, and 3 that is greatly

reduced with Alternative 4 in Zone 3 (the Thompson Divide area).

Muehulenbacks analysis of hydraulic fracturing related truck traffic and accidents

(http://www.rff.org/files/sharepoint/Documents/Events/Seminars/140410-Muehlenbachs-

presentation.pdf) found increased truck traffic associated with fracking wells in

Pennsylvania resulted in a statistically significant increase in accidents per well drilled. A

substantial portion of these accidents resulted in fatalities due to the size of the trucks

involved in the accident. This adverse effect on safety should be noted in conjunction

with Alternatives #1, #2 and #3. The avoidance of much of this adverse effect on safety

with Alternative 4 should be acknowledged by BLM.

Thus, I would recommend BLM cite these studies in its Chapter 3 and 4 so BLM

has something more than just professional judgment that increased traffic will reduce

property values.

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E. Possible Loss of Non Use Values if Roadless Areas are allowed to be Disturbed

While there is some contention as to whether roads could be constructed in Inventoried

Roadless Areas within the lease areas, we demonstrate a simple Benefit Transfer to

calculate the Passive Use/Non Use Values (existence values to the current generation and

bequest values to future generations) if these areas were disturbed2. Here we present a

worst case scenario of adverse effects to approximately 100,000 acres of Roadless acres

within the impact area. BLM, using GIS, can refine the acreage estimate and refine my

calculation of Passive Use/Non Use Values accordingly.

Walsh, et al. conducted a contingent valuation method (CVM) survey of Colorado

households to ask their willingness to pay (WTP) for preserving alternative quantities of

Wilderness and roadless areas. WTP is a federally approved value for measuring non

market values, and is used by agencies ranging from U.S. EPA (2000) to Office of

Management and Budget (OMB, 1992; 2000). In the published journal article, Walsh, et

al.’s Non Use Value (what the article calls Preservation Value) is:

WTP (per Household, 1980 $) =

$9.17 +$4.1854(Millions of Acres) –.1919(Millions of Acres Squared)

One of the acreages the survey asked Colorado households their WTP for was preserving

5 million acres of Roadless Areas as Wilderness to preserve these areas wilderness

values. While Colorado only has 3.6 million acres of Wilderness, to be conservative we

applied this equation assuming the WTP for 5 million acres as the baseline and calculated

that value. We then subtracted from the 5 million acres the loss of 100,000 acres of

Roadless acreage that might potentially be disturbed with Alternatives #1 and #2 (as

noted BLM can use GIS analysis and refine the acreage used).

The per household value updated from 1980 dollars to 2014 dollars for 5 million

acres is $72.69. The per household value for 4.9 million acres is $72.03. Thus a loss of

100,000 Roadless Acres amounts to loss of $.66 per Colorado household. As Walsh, et al.

noted Non Use values are pure public goods available to all Colorado households.

Therefore Walsh, et al. generalized their values in the original published article to all

2 The standard lease terms incorporate the 2001 Colorado Roadless Rule by reference, and so those

protections would apply. But the lessees appear to be disputing this and asserting that their leases conveyed

the right to build roads in Inventoried Roadless Areas. While this is incorrect, the analysis depicts what the

impacts would be if road building is allowed.

17

Colorado households. Doing this for the loss of $.66 per household leads to a loss of $1.3

million annually. The loss in Passive Use/Non Use Value should be incorporated in the

Socioeconomic section of the FEIS as an economic cost of Alternatives #1, #2, and #3 or

$1.3 million of benefits for Alternative #4.

F. BLM Socioeconomic Analysis in relation to Headwaters reports.

BLM generally acknowledges some of the effects analyzed by Headwaters in their series

of reports that I reviewed (Headwaters, 2008; 2011). Headwaters (2011), indicates there

are three effects of emphasizing oil and gas on an economy and community: (a)

dependence on oil and gas for a large part of a county’s income, results in relatively more

variability in per capita income in an oil and gas dependent county, and in some cases

long term decreases over time; (b) lower educational achievement. The availability of

high paying oil and gas jobs may cause college age individuals to forego college. When

the oil and gas boom is over, those without college education are unprepared for many

jobs that require college degrees. This may be one of the factors leading to lower per

capita incomes in a county over time; and (c) increased crime, hence higher law

enforcement and social services costs. As succinctly stated by Headwaters (2011:1)

“Long-term oil and gas specialization is observed to have negative effects on change in

per capita income, crime rate and education rate”.

The BLM socioeconomic analysis mentions (c) but does not give much attention

to long term income fluctuations and trends with oil and gas (a). Nor does BLM provide

any detailed discussion of the long term effects of reduced educational attainment on long

term economic viability of communities (b). A short discussion and citations to

Headwaters reports would be appropriate.

G. Non-Market Effects of Drilling on Recreation Quality and Substitution

Assumption

As noted above BLM restricts its analysis of the effects of oil and gas development on

recreation to acres of surface disturbance without looking at the spillover impacts visible

or heard at wide ranging distances in an otherwise quiet area (especially Zone 3). Further

BLM’s statement that there is not much current recreation use and there are opportunities

for site substitution (hence no changes in visitor spending or socioeconomic impacts),

18

neglects the non-market values provided by Zone 3. It is the low visitor use levels that

make Zone 3 especially a valuable recreation resource: the solitude and quiet. Many other

areas in the WRNF’s Sopris Ranger District have significant crowding. Substitution of

displaced Thompson Divide visitors to those already crowded areas raises congestion

costs imposed on other visitors in these other areas. In addition the recreation benefit to

the Thompson Divide visitors is lower than the current situation if the Thompson Divide

visitors substitute from less crowded areas (Thompson Divide—Zone 3) to more crowded

areas. A well documented non market value of recreation is related to lack of crowding

and congestion (Walsh, and Gilliam, 1982; Loomis and Walsh, 1997: 104-107). This lack

of crowding and congestion is something that Zone 3 currently provides and would

continue to provide with Alternative #4. Site substitution also usually involves higher

travel costs to the substitute site than the current site. Therefore the higher travel cost for

site substitution and loss of the non market value of solitude provided by Zone 3,

(Thompson Divide) should be acknowledged by BLM in its Socioeconomic analysis and

the references cited in this section should be incorporated into the FEIS.

H. Effects of the Alternatives on County Tax Revenues

BLM quantifies the changes in county revenues in Chapter 4, Section 4.17 for Alternative

#1 (BLM indicates Alternative #2 would be quite similar) and Alternative #4. BLM

acknowledges that some of this revenue “may be expected to offset road improvements

that might be necessary from the increased use of county roads by well construction

traffic” (Page 4.17). However, this is observation is not carried through much of the

remainder of the discussion of county revenues. It is not mentioned in the summary

(pages 4.17-30 to 31). In addition, the concept of net tax revenue is not explicitly

discussed. The current BLM analysis and summary indicates that relative to Alternative

#1, Alternative #4 results in a “…decrease of approximately $5 million compared to the

Alternative #1—No Action” (page 31). But no mention is made that there would also be a

reduction in road maintenance, law enforcement or social services costs with Alternative

4. Thus, the actual net discretionary tax revenues lost to the counties from Alternative #4

is likely to be less than the $5 million, as there will be cost savings in reduced law

enforcement, transportation improvements and maintenance and social services that

19

would be required with Alternative #1. The reduced expenditures to the county with

Alternative #4 should be mentioned in the FEIS. Finally, Headwaters (2008:3) notes that

oil and gas tax revenues to the county are a volatile revenue source and “…the lag between

the activities that create new demands and when property tax revenues are actually received

makes it difficult to keep pace with surging service demands.” These two points regarding tax

revenues from oil and gas should be mentioned in the FEIS.

Conclusion

This report provides quantitative estimates and references to the literature that provide

more detail and evidence of non market economic benefits (e.g., recreation, hunting, and

property values) of Alternative #4 and corresponding non market economic costs of

Alternatives #1, #2 and #3. Incorporation of the analysis and references provided in this

report into the FEIS would provide a more comprehensive analysis of the economic

effects of Alternatives #1, #2, #3, #4 and #5.

20

References

BBC. 2008. The Economic Impacts of Hunting, Fishing and Wildlife Watching in

Colorado. BBC Research and Consulting, Denver, CO.

BBC. 2013. The Economic Contribution of the Thompson Divide to Western Colorado.

BBC Research and Consulting, Denver, CO.

BLM, Second Modified Decision Record for Williams Production RMT Company, Carr

Draw Federal POD III West. Environmental Assessment WY-070-09-066. Signed by

Field Manager, Buffalo Field Office, 7-2-2010.

BLM. Third Modified Decision Record for Williams Production RMT Company, Carr

Draw Federal POD III West. Environmental Assessment WY-070-09-066. Signed by

Field Manager 1-21-2011.

BLM—Buffalo Field Office, Williams Production Company Carr Draw Federal POD V

Addition II, Environmental Assessment WY-070-EA09-123. Signed by Field Manager

July 25, 2009. Accessed December 30, 2015 at:

http://www.blm.gov/style/medialib/blm/wy/information/NEPA/bfodocs/pods/williams.Pa

r.21794.File.dat/CarrDrawVaddII_EA.pdf

BLM. 2013. Guidance on Estimating Nonmarket Environmental Values. Instruction

Memorandum 2013-131.

BLM. 2013. Best Management Practices for Reducing Visual Impacts of Renewable

Energy Facilities on BLM Administered Lands.

http://www.blm.gov/style/medialib/blm/wo/MINERALS__REALTY__AND_RESOURC

E_PROTECTION_/energy/renewable_references.Par.1568.File.dat/RenewableEnergyVis

ualImpacts_BMPs.pdf

Deller, S., T-H Tsai, D. Marcouiller and D. English. 2001. American Journal of

Agricultural Economics 83(2): 352-365.

Ellingson, L., A. Seidl and C.J. Mcklow. Tourists’ Value of Routt County Working

Landscape. Economic Development Report EDR06-06. Dept. of Agricultural and

Resource Economics, Colorado State University.

http://webdoc.agsci.colostate.edu/DARE/EDR/EDR06-06.pdf

Freeman, M. (2003). The Measurement of Environmental and Resource Values, 2nd

Edition. Resources for the Future Press. Washington DC.

Headwaters. 2008. Impacts of Energy Development in Colorado with a Case Study of

Mesa and Garfield Counties. November 2008.

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Headwaters. 2011. Fossil Fuel Extraction and Western Economies. Bozeman, MT. http://headwaterseconomics.org/energy/western/maximizing-benefits

Headwaters. 2013. Long-term Effects of Income Specializing in Oil and Gas Extraction:

the U.S. West 1980-2011. http://dev.headwaterseconomics.org/wphw/wp-

content/uploads/OilAndGasSpecialization_Manuscript_2013.pdf

Loomis, J. and R. Walsh. 1997. Recreation Economic Decisions: Comparing Benefits and

Cost, Venture Press, State College, PA.

Orens, A. and A. Seidl. 2004. Winter Tourism and Land Development in Gunnison,

Colorado. Economic Development Report EDR 04-10. Dept. of Agricultural and

Resource Economics, Colorado State University

http://webdoc.agsci.colostate.edu/DARE/EDR/EDR04-10.pdf

Sawyer, H., R. Nielson, F. Lindzey and L. McDonald. 2006. Winter Habitat Selection of

Mule Deer Before and During Development of a Natural Gas Field. Journal of Wildlife

Management April 2006: pages 396-403. http://www.bioone.org/doi/abs/10.2193/0022-

541X(2006)70%5B396:WHSOMD%5D2.0.CO%3B2

Taylor, L.O. (2003) The hedonic method. In P.A. Champ, Boyle K.J., & Brown T.C.

(Eds.), A Primer on Nonmarket Valuation (pp. 331-393). Norwell, MA: Kluwer

Academic Publishers.

U.S. Environmental Protection Agency.2000. Guidelines for Preparing Economic

Analyses. September 2000. Washington DC.

USDA Forest Service, 2014. White River National Forest Oil and Gas Leasing Record of

Decision. http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/stelprd3824509.pdf

U.S. Office of Management and Budget. 1992. Guidelines and Discount Rates for

Benefit-Cost Analysis of Federal Programs. Washington DC. Available at

http://www.whitehouse.gov/omb/circulars_a094

U.S. Office of Management and Budget. 2000. Guidelines to Standardize Measures of

Costs and Benefits and the Format of Accounting Statements. M-00-08, from Jacob Lew,

Director. Washington DC.

Walsh, R. and L. Gilliam. 1982. Benefits of Wilderness Expansion with Excess Demand

for Indian Peaks, Western Journal of Agricultural Economics 7(1): 1-12.

Walsh, R., J. Loomis and R. Gillman. 1984. Valuing Option, Existence and Bequest

Demands for Wilderness. Land Economics 60(1): 24-29.

22

John B. Loomis, Professor 10-2015

Department of Agricultural and Resource Economics

Colorado State University, Fort Collins, CO 80523-1172

(970) 226-4052 John.Loomis@colostate.edu

http://dare.agsci.colostate.edu/people/faculty/dr-john-b-loomis/

EDUCATION Colorado State University Ph.D. Economics 12/83

(Fields: Environmental & Natural Resource Economics, Public Finance and Forest Economics)

California State University, Northridge M.A. Economics 1/77

California State University, Northridge B.A. Economics 8/75

AWARDS AND HONORS

Fellow, Agricultural and Applied Economics Association (AAEA) 2014

Fellow, Association of Environmental and Resource Economists (AERE), 2013

Meritorious Teaching Award, College of Agricultural Sciences, Colorado State University, 2013

Distinguished Career Award, College of Agricultural Sciences, Colorado State University, 2012

Publication of Enduring Quality, Agricultural and Applied Economics Association (AAEA), 2009.

Outstanding Journal Article for 2008, Journal of Agricultural and Applied Economics.

Distinguished Scholar, Western Agricultural Economics Association, 2004

Distinguished Publication Award, US Forest Service, Pacific Southwest Research Station, 2000

Quality Performance Award, U.S. Fish and Wildlife Service, 1985

PUBLICATIONS Four books, Integrated Public Lands Management published by Columbia University Press,

Recreation Economic Decisions, 2nd ed., Environmental Policy Analysis for Decision Making,

Determining the Economic Value of Water, 2nd Edition plus 244 articles in journals including

Land Economics, American Journal of Agricultural Economics, Public Finance Quarterly, Review

of Economics and Statistics, Environmental & Resource Economics, Journal of Forestry, Water

Resources Research and Journal of Environmental Economics and Management. Have published

75 book chapters/proceedings paper and 27 Experiment Station or Government Reports.

POSITIONS IN PROFESSIONAL SOCIETIES & ADVISORY ROLES

Associate Editor, American Journal of Agricultural Economics (2012-2015)

Independent External Panel Reviewer, U.S. Army Corps of Engineers, 2010-present

Vice President, Association of Environmental and Resource Economists (2000-01)

Co-Editor, Association of Environmental and Resource Economists Newsletter (1998 to present)

Associate Editor (1989-92), Water Resources Research

Editorial Council Member (1998-2000), Journal of Agricultural and Resource Economics

WORK EXPERIENCE

8/93 to present: Professor, Department of Agricultural and Resource Economics, Colorado State

University, Fort Collins, CO.

8/85 to 7/93: Associate Professor, Division of Environmental Studies & Department of Agricultural

Economics, University of California, Davis, CA.

3/80 to 7/85: Economist, U.S. Department of Interior, U.S. Fish and Wildlife Service, Ft. Collins, CO.

8/77 to 2/80 Regional Economist, Bureau of Land Management, Moab District Office, Moab, UT